Hi Julie,

Thank you for the reply.

To run the analysis in a less stringent mode, I've tried the parameters (see below), however the off-target that's predicted in the individual sample is not present in the concatenated sample. keepPeaksInBothStrandsOnly = FALSE and min.peak.score.1strandOnly=1L

There's no option for min.read.coverage and min.umi.count in the version of the package I installed. Can you please let me know if there's an option to keep duplicate reads?

Thank you for all the help.

> sessionInfo()
R version 4.1.3 (2022-03-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Monterey 12.2

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] BSgenome.Hsapiens.UCSC.hg38_1.4.4 BSgenome_1.62.0                  
 [3] rtracklayer_1.54.0                Biostrings_2.62.0                
 [5] XVector_0.34.0                    GUIDEseq_1.24.0                  
 [7] GenomicRanges_1.46.1              GenomeInfoDb_1.30.1              
 [9] IRanges_2.28.0                    S4Vectors_0.32.4                 
[11] BiocGenerics_0.40.0              

loaded via a namespace (and not attached):
  [1] colorspace_2.0-3            seqinr_4.2-16               rjson_0.2.21               
  [4] ellipsis_0.3.2              futile.logger_1.4.3         base64enc_0.1-3            
  [7] rstudioapi_0.13             ChIPpeakAnno_3.28.1         hash_2.2.6.2               
 [10] bit64_4.0.5                 AnnotationDbi_1.56.2        fansi_1.0.3                
 [13] xml2_1.3.3                  splines_4.1.3               cachem_1.0.6               
 [16] zeallot_0.1.0               ade4_1.7-19                 jsonlite_1.8.0             
 [19] Rsamtools_2.10.0            dbplyr_2.2.0                png_0.1-7                  
 [22] tfruns_1.5.0                graph_1.72.0                compiler_4.1.3             
 [25] httr_1.4.3                  assertthat_0.2.1            Matrix_1.4-1               
 [28] fastmap_1.1.0               lazyeval_0.2.2              limma_3.50.3               
 [31] cli_3.3.0                   formatR_1.12                prettyunits_1.1.1          
 [34] tools_4.1.3                 gtable_0.3.0                glue_1.6.2                 
 [37] GenomeInfoDbData_1.2.7      dplyr_1.0.9                 mltools_0.3.5              
 [40] rappdirs_0.3.3              Rcpp_1.0.8.3                Biobase_2.54.0             
 [43] vctrs_0.4.1                 rhdf5filters_1.6.0          multtest_2.50.0            
 [46] stringr_1.4.0               lifecycle_1.0.1             restfulr_0.0.14            
 [49] ensembldb_2.18.4            XML_3.99-0.9                InteractionSet_1.22.0      
 [52] zlibbioc_1.40.0             MASS_7.3-57                 scales_1.2.0               
 [55] hms_1.1.1                   MatrixGenerics_1.6.0        ProtGenerics_1.26.0        
 [58] parallel_4.1.3              SummarizedExperiment_1.24.0 RBGL_1.70.0                
 [61] rhdf5_2.38.1                AnnotationFilter_1.18.0     lambda.r_1.2.4             
 [64] yaml_2.3.5                  curl_4.3.2                  memoise_2.0.1              
 [67] reticulate_1.25             ggplot2_3.3.6               keras_2.9.0                
 [70] biomaRt_2.50.3              stringi_1.7.6               RSQLite_2.2.14             
 [73] tensorflow_2.9.0            BiocIO_1.4.0                GenomicFeatures_1.46.5     
 [76] filelock_1.0.2              BiocParallel_1.28.3         rlang_1.0.2                
 [79] pkgconfig_2.0.3             matrixStats_0.62.0          bitops_1.0-7               
 [82] lattice_0.20-45             purrr_0.3.4                 Rhdf5lib_1.16.0            
 [85] GenomicAlignments_1.30.0    bit_4.0.4                   tidyselect_1.1.2           
 [88] magrittr_2.0.3              R6_2.5.1                    generics_0.1.2             
 [91] DelayedArray_0.20.0         DBI_1.1.2                   pillar_1.7.0               
 [94] whisker_0.4                 survival_3.3-1              KEGGREST_1.34.0            
 [97] RCurl_1.98-1.6              tibble_3.1.7                crayon_1.5.1               
[100] futile.options_1.0.1        utf8_1.2.2                  BiocFileCache_2.2.1        
[103] progress_1.2.2              grid_4.1.3                  data.table_1.14.2          
[106] blob_1.2.3                  digest_0.6.29               VennDiagram_1.7.3          
[109] regioneR_1.26.1             CRISPRseek_1.34.2           munsell_0.5.0

Hi Julie,

Thank you for the reply.

I installed the newer version and re-ran the function with the parameters you suggested:

GUIDEseqAnalysis(alignment.inputfile = bamfile , umi.inputfile = umifile,
                 alignment.format = c("bam"),
                 BSgenomeName = Hsapiens,
                 gRNA.file = gRNAs, n.cores.max = 4,min.mapping.quality = 15L,max.R1.len = 150L, max.R2.len =150L,
                 min.reads = 1,min.SNratio = 2, min.read.coverage = 1L,min.umi.count = 1,
                 maxP = 0.01,window.size = 25L,step = 25L,
                 plus.strand.start.gt.minus.strand.end = FALSE,distance.threshold = 1000,
                 upstream = 50L, downstream = 50L, PAM.size = 3, gRNA.size = 20,
                 PAM = "NGG", PAM.pattern = "(NGG)$", max.mismatch = 6,
                 outputDir = outputDir,
                 orderOfftargetsBy = "predicted_cleavage_score",
                 allowed.mismatch.PAM = 2, overwrite = TRUE,keepPeaksInBothStrandsOnly = FALSE)

However, a specific off-target of interest is still not being detected. May be the removal of duplicate reads is resulting in no reads in that locus due to the technical samples being merged. Is there a way to run the tool without removing duplicate reads for testing?

Thanks Sharvari